Premium
Fully unsupervised inter‐individual IR spectral histology of paraffinized tissue sections of normal colon
Author(s) -
Nguyen Thi Nguyet Que,
Jeannesson Pierre,
Groh Audrey,
Piot Olivier,
Guenot Dominique,
Gobinet Cyril
Publication year - 2016
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201500285
Subject(s) - histology , artificial intelligence , h&e stain , pattern recognition (psychology) , computer science , lamina propria , joint (building) , eosin , pathology , computer vision , biomedical engineering , mathematics , staining , medicine , epithelium , architectural engineering , engineering
In label‐free Fourier‐transform infrared histology, spectral images are individually recorded from tissue sections, pre‐processed and clustered. Each single resulting color‐coded image is annotated by a pathologist to obtain the best possible match with tissue structures revealed after Hematoxylin‐Eosin staining. However, the main limitations of this approach are the empirical choice of the number of clusters in unsupervised classification, and the marked color heterogeneity between the clustered spectral images. Here, using normal murine and human colon tissues, we developed an automatic multi‐image spectral histology to simultaneously analyze a set of spectral images (8 images mice samples and 72 images human ones). This procedure consisted of a joint Extended Multiplicative Signal Correction (EMSC) to numerically deparaffinize the tissue sections, followed by an automated joint K‐Means (KM) clustering using the hierarchical double application of Pakhira‐Bandyopadhyay‐Maulik (PBM) validity index. Using this procedure, the main murine and human colon histological structures were correctly identified at both the intra‐ and the inter‐individual levels, especially the crypts, secreted mucus, lamina propria and submucosa. Here, we show that batched multi‐image spectral histology procedure is insensitive to the reference spectrum but highly sensitive to the paraffin model of joint EMSC. In conclusion, combining joint EMSC and joint KM clustering by double PBM application allows to achieve objective and automated batched multi‐image spectral histology.